Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory402.5 KiB
Average record size in memory664.8 B

Variable types

Numeric11
Categorical5
Text2
URL2

Alerts

type has constant value "audio_features" Constant
time_signature is highly imbalanced (65.8%) Imbalance
key has 65 (10.5%) zeros Zeros
instrumentalness has 338 (54.5%) zeros Zeros

Reproduction

Analysis started2024-12-02 11:05:09.364292
Analysis finished2024-12-02 11:05:16.719901
Duration7.36 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

danceability
Real number (ℝ)

Distinct221
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63440323
Minimum0.233
Maximum0.936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:16.782237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.233
5-th percentile0.3728
Q10.517
median0.66
Q30.747
95-th percentile0.8743
Maximum0.936
Range0.703
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.15674279
Coefficient of variation (CV)0.24707124
Kurtosis-0.56550224
Mean0.63440323
Median Absolute Deviation (MAD)0.117
Skewness-0.29292309
Sum393.33
Variance0.024568302
MonotonicityNot monotonic
2024-12-02T11:05:16.857834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.777 15
 
2.4%
0.554 12
 
1.9%
0.494 11
 
1.8%
0.521 10
 
1.6%
0.739 9
 
1.5%
0.392 9
 
1.5%
0.747 9
 
1.5%
0.467 8
 
1.3%
0.472 8
 
1.3%
0.7 8
 
1.3%
Other values (211) 521
84.0%
ValueCountFrequency (%)
0.233 2
0.3%
0.252 1
 
0.2%
0.264 3
0.5%
0.267 1
 
0.2%
0.271 3
0.5%
0.285 2
0.3%
0.288 3
0.5%
0.296 2
0.3%
0.31 2
0.3%
0.314 4
0.6%
ValueCountFrequency (%)
0.936 1
 
0.2%
0.929 1
 
0.2%
0.928 1
 
0.2%
0.924 2
0.3%
0.921 1
 
0.2%
0.92 4
0.6%
0.916 3
0.5%
0.9 4
0.6%
0.898 3
0.5%
0.897 1
 
0.2%

energy
Real number (ℝ)

Distinct226
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62426968
Minimum0.0756
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:16.934737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0756
5-th percentile0.31675
Q10.507
median0.645
Q30.757
95-th percentile0.88765
Maximum0.986
Range0.9104
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.17880485
Coefficient of variation (CV)0.28642244
Kurtosis-0.2894668
Mean0.62426968
Median Absolute Deviation (MAD)0.1325
Skewness-0.4721841
Sum387.0472
Variance0.031971173
MonotonicityNot monotonic
2024-12-02T11:05:16.994532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.783 15
 
2.4%
0.808 13
 
2.1%
0.555 12
 
1.9%
0.413 11
 
1.8%
0.757 10
 
1.6%
0.471 10
 
1.6%
0.592 10
 
1.6%
0.507 9
 
1.5%
0.247 8
 
1.3%
0.704 8
 
1.3%
Other values (216) 514
82.9%
ValueCountFrequency (%)
0.0756 2
0.3%
0.129 1
0.2%
0.138 1
0.2%
0.155 2
0.3%
0.157 1
0.2%
0.158 1
0.2%
0.162 1
0.2%
0.192 1
0.2%
0.216 1
0.2%
0.223 1
0.2%
ValueCountFrequency (%)
0.986 1
 
0.2%
0.958 1
 
0.2%
0.957 1
 
0.2%
0.946 1
 
0.2%
0.934 1
 
0.2%
0.917 2
 
0.3%
0.91 5
0.8%
0.908 2
 
0.3%
0.907 7
1.1%
0.904 3
0.5%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3677419
Minimum0
Maximum11
Zeros65
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:17.056104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation3.7178682
Coefficient of variation (CV)0.6926317
Kurtosis-1.364201
Mean5.3677419
Median Absolute Deviation (MAD)4
Skewness0.0099594861
Sum3328
Variance13.822544
MonotonicityNot monotonic
2024-12-02T11:05:17.261069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 90
14.5%
6 73
11.8%
0 65
10.5%
11 64
10.3%
9 57
9.2%
10 51
8.2%
2 49
7.9%
7 46
7.4%
5 40
6.5%
4 38
6.1%
Other values (2) 47
7.6%
ValueCountFrequency (%)
0 65
10.5%
1 90
14.5%
2 49
7.9%
3 15
 
2.4%
4 38
6.1%
5 40
6.5%
6 73
11.8%
7 46
7.4%
8 32
 
5.2%
9 57
9.2%
ValueCountFrequency (%)
11 64
10.3%
10 51
8.2%
9 57
9.2%
8 32
5.2%
7 46
7.4%
6 73
11.8%
5 40
6.5%
4 38
6.1%
3 15
 
2.4%
2 49
7.9%

loudness
Real number (ℝ)

Distinct267
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.6318419
Minimum-21.432
Maximum-0.705
Zeros0
Zeros (%)0.0%
Negative620
Negative (%)100.0%
Memory size9.7 KiB
2024-12-02T11:05:17.307477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-21.432
5-th percentile-10.759
Q1-7.8
median-6.199
Q3-4.86
95-th percentile-3.57055
Maximum-0.705
Range20.727
Interquartile range (IQR)2.94

Descriptive statistics

Standard deviation2.4393248
Coefficient of variation (CV)-0.36782011
Kurtosis3.5383367
Mean-6.6318419
Median Absolute Deviation (MAD)1.548
Skewness-1.2114196
Sum-4111.742
Variance5.9503057
MonotonicityNot monotonic
2024-12-02T11:05:17.362660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.477 12
 
1.9%
-4.169 12
 
1.9%
-10.432 11
 
1.8%
-7.777 10
 
1.6%
-6.707 9
 
1.5%
-12.002 8
 
1.3%
-7.507 8
 
1.3%
-10.171 8
 
1.3%
-5.692 8
 
1.3%
-5.96 8
 
1.3%
Other values (257) 526
84.8%
ValueCountFrequency (%)
-21.432 1
 
0.2%
-18.902 2
 
0.3%
-14.073 1
 
0.2%
-13.824 1
 
0.2%
-13.158 1
 
0.2%
-12.85 2
 
0.3%
-12.356 2
 
0.3%
-12.295 1
 
0.2%
-12.151 1
 
0.2%
-12.002 8
1.3%
ValueCountFrequency (%)
-0.705 1
 
0.2%
-0.959 1
 
0.2%
-2.4 1
 
0.2%
-2.412 2
0.3%
-2.655 3
0.5%
-2.76 1
 
0.2%
-2.768 2
0.3%
-2.81 2
0.3%
-2.844 1
 
0.2%
-2.851 1
 
0.2%

mode
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
1
413 
0
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 413
66.6%
0 207
33.4%

Length

2024-12-02T11:05:17.410411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:05:17.446437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 413
66.6%
0 207
33.4%

Most occurring characters

ValueCountFrequency (%)
1 413
66.6%
0 207
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 413
66.6%
0 207
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 413
66.6%
0 207
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 413
66.6%
0 207
33.4%

speechiness
Real number (ℝ)

Distinct227
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.090477903
Minimum0.0252
Maximum0.555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:17.490095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0252
5-th percentile0.027785
Q10.0353
median0.04505
Q30.0964
95-th percentile0.325
Maximum0.555
Range0.5298
Interquartile range (IQR)0.0611

Descriptive statistics

Standard deviation0.098533608
Coefficient of variation (CV)1.0890351
Kurtosis4.6760656
Mean0.090477903
Median Absolute Deviation (MAD)0.01425
Skewness2.2346545
Sum56.0963
Variance0.0097088719
MonotonicityNot monotonic
2024-12-02T11:05:17.544915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0368 13
 
2.1%
0.26 12
 
1.9%
0.0254 12
 
1.9%
0.033 10
 
1.6%
0.0278 10
 
1.6%
0.0304 10
 
1.6%
0.0431 9
 
1.5%
0.039 9
 
1.5%
0.0358 8
 
1.3%
0.0603 8
 
1.3%
Other values (217) 519
83.7%
ValueCountFrequency (%)
0.0252 3
 
0.5%
0.0254 12
1.9%
0.0256 1
 
0.2%
0.0262 2
 
0.3%
0.0264 2
 
0.3%
0.0265 3
 
0.5%
0.0271 3
 
0.5%
0.0272 2
 
0.3%
0.0273 2
 
0.3%
0.0275 1
 
0.2%
ValueCountFrequency (%)
0.555 2
 
0.3%
0.461 6
1.0%
0.459 3
0.5%
0.436 2
 
0.3%
0.407 3
0.5%
0.401 1
 
0.2%
0.363 1
 
0.2%
0.359 5
0.8%
0.358 2
 
0.3%
0.348 4
0.6%

acousticness
Real number (ℝ)

Distinct252
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.262329
Minimum0.000175
Maximum0.968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:17.600699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.000175
5-th percentile0.00306
Q10.0441
median0.164
Q30.465
95-th percentile0.8
Maximum0.968
Range0.967825
Interquartile range (IQR)0.4209

Descriptive statistics

Standard deviation0.26888525
Coefficient of variation (CV)1.0249925
Kurtosis-0.32856069
Mean0.262329
Median Absolute Deviation (MAD)0.144
Skewness0.95461392
Sum162.64398
Variance0.072299276
MonotonicityNot monotonic
2024-12-02T11:05:17.656941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0283 12
 
1.9%
0.214 12
 
1.9%
0.682 11
 
1.8%
0.308 10
 
1.6%
0.287 9
 
1.5%
0.656 8
 
1.3%
0.257 8
 
1.3%
0.151 8
 
1.3%
0.0584 8
 
1.3%
0.612 8
 
1.3%
Other values (242) 526
84.8%
ValueCountFrequency (%)
0.000175 4
0.6%
0.000587 3
0.5%
0.000588 1
 
0.2%
0.000784 1
 
0.2%
0.000938 1
 
0.2%
0.00115 1
 
0.2%
0.0013 1
 
0.2%
0.002 1
 
0.2%
0.00239 3
0.5%
0.00273 1
 
0.2%
ValueCountFrequency (%)
0.968 2
0.3%
0.958 2
0.3%
0.949 1
 
0.2%
0.945 1
 
0.2%
0.933 3
0.5%
0.929 1
 
0.2%
0.924 1
 
0.2%
0.918 1
 
0.2%
0.913 1
 
0.2%
0.897 1
 
0.2%

instrumentalness
Real number (ℝ)

Zeros 

Distinct133
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013385405
Minimum0
Maximum0.914
Zeros338
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:17.711038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.4875 × 10-5
95-th percentile0.0575
Maximum0.914
Range0.914
Interquartile range (IQR)8.4875 × 10-5

Descriptive statistics

Standard deviation0.071836035
Coefficient of variation (CV)5.3667436
Kurtosis78.389386
Mean0.013385405
Median Absolute Deviation (MAD)0
Skewness8.134609
Sum8.2989509
Variance0.005160416
MonotonicityNot monotonic
2024-12-02T11:05:17.764297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 338
54.5%
6.72 × 10-511
 
1.8%
0.000253 9
 
1.5%
0.000271 8
 
1.3%
2.56 × 10-68
 
1.3%
0.0608 8
 
1.3%
1.36 × 10-56
 
1.0%
5.36 × 10-66
 
1.0%
0.0016 5
 
0.8%
1.9 × 10-55
 
0.8%
Other values (123) 216
34.8%
ValueCountFrequency (%)
0 338
54.5%
1.18 × 10-61
 
0.2%
1.25 × 10-62
 
0.3%
1.34 × 10-62
 
0.3%
1.44 × 10-62
 
0.3%
1.45 × 10-61
 
0.2%
1.61 × 10-64
 
0.6%
1.88 × 10-61
 
0.2%
2 × 10-61
 
0.2%
2.08 × 10-61
 
0.2%
ValueCountFrequency (%)
0.914 1
 
0.2%
0.829 1
 
0.2%
0.513 1
 
0.2%
0.422 1
 
0.2%
0.402 4
0.6%
0.336 2
0.3%
0.302 1
 
0.2%
0.3 2
0.3%
0.272 1
 
0.2%
0.144 1
 
0.2%

liveness
Real number (ℝ)

Distinct201
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17101129
Minimum0.0333
Maximum0.805
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:17.831619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0333
5-th percentile0.0622
Q10.0967
median0.127
Q30.205
95-th percentile0.4173
Maximum0.805
Range0.7717
Interquartile range (IQR)0.1083

Descriptive statistics

Standard deviation0.11711694
Coefficient of variation (CV)0.68484917
Kurtosis5.1456193
Mean0.17101129
Median Absolute Deviation (MAD)0.043
Skewness1.9985285
Sum106.027
Variance0.013716378
MonotonicityNot monotonic
2024-12-02T11:05:17.888427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.104 19
 
3.1%
0.193 18
 
2.9%
0.122 16
 
2.6%
0.111 14
 
2.3%
0.355 12
 
1.9%
0.159 12
 
1.9%
0.14 11
 
1.8%
0.12 11
 
1.8%
0.11 10
 
1.6%
0.117 10
 
1.6%
Other values (191) 487
78.5%
ValueCountFrequency (%)
0.0333 4
0.6%
0.0378 2
 
0.3%
0.0398 2
 
0.3%
0.0407 5
0.8%
0.0426 1
 
0.2%
0.0436 2
 
0.3%
0.0466 1
 
0.2%
0.0483 1
 
0.2%
0.0523 1
 
0.2%
0.0536 3
0.5%
ValueCountFrequency (%)
0.805 1
 
0.2%
0.789 1
 
0.2%
0.749 1
 
0.2%
0.738 1
 
0.2%
0.673 1
 
0.2%
0.638 1
 
0.2%
0.567 1
 
0.2%
0.55 1
 
0.2%
0.527 1
 
0.2%
0.505 4
0.6%

valence
Real number (ℝ)

Distinct233
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52066839
Minimum0.0398
Maximum0.965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:17.946413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0398
5-th percentile0.156
Q10.314
median0.535
Q30.711
95-th percentile0.92735
Maximum0.965
Range0.9252
Interquartile range (IQR)0.397

Descriptive statistics

Standard deviation0.23643679
Coefficient of variation (CV)0.45410244
Kurtosis-1.0702971
Mean0.52066839
Median Absolute Deviation (MAD)0.197
Skewness0.030299421
Sum322.8144
Variance0.055902354
MonotonicityNot monotonic
2024-12-02T11:05:18.008597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.939 12
 
1.9%
0.372 12
 
1.9%
0.273 11
 
1.8%
0.535 10
 
1.6%
0.838 10
 
1.6%
0.471 9
 
1.5%
0.785 9
 
1.5%
0.191 9
 
1.5%
0.219 8
 
1.3%
0.57 8
 
1.3%
Other values (223) 522
84.2%
ValueCountFrequency (%)
0.0398 1
 
0.2%
0.0554 3
 
0.5%
0.0556 1
 
0.2%
0.0998 1
 
0.2%
0.12 5
0.8%
0.126 8
1.3%
0.131 1
 
0.2%
0.135 3
 
0.5%
0.138 1
 
0.2%
0.145 1
 
0.2%
ValueCountFrequency (%)
0.965 1
 
0.2%
0.963 1
 
0.2%
0.962 1
 
0.2%
0.961 1
 
0.2%
0.96 4
 
0.6%
0.957 5
0.8%
0.939 12
1.9%
0.935 4
 
0.6%
0.934 2
 
0.3%
0.927 2
 
0.3%

tempo
Real number (ℝ)

Distinct270
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.91456
Minimum66.495
Maximum194.055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:18.068913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum66.495
5-th percentile81.012
Q1103.963
median118.244
Q3143.694
95-th percentile173.40825
Maximum194.055
Range127.56
Interquartile range (IQR)39.731

Descriptive statistics

Standard deviation27.461516
Coefficient of variation (CV)0.22341956
Kurtosis-0.54842242
Mean122.91456
Median Absolute Deviation (MAD)20.734
Skewness0.33539752
Sum76207.026
Variance754.13487
MonotonicityNot monotonic
2024-12-02T11:05:18.125154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149.027 12
 
1.9%
108.548 12
 
1.9%
94.938 11
 
1.8%
157.969 10
 
1.6%
96.063 9
 
1.5%
104.978 8
 
1.3%
116.712 8
 
1.3%
119.973 8
 
1.3%
115.94 8
 
1.3%
148.101 8
 
1.3%
Other values (260) 526
84.8%
ValueCountFrequency (%)
66.495 2
0.3%
67.086 4
0.6%
67.528 1
 
0.2%
77.002 1
 
0.2%
77.639 1
 
0.2%
78.111 3
0.5%
78.139 2
0.3%
78.558 1
 
0.2%
79.526 1
 
0.2%
79.979 1
 
0.2%
ValueCountFrequency (%)
194.055 1
 
0.2%
192.004 2
0.3%
191.868 1
 
0.2%
184.784 1
 
0.2%
184.115 3
0.5%
183.798 1
 
0.2%
183.546 1
 
0.2%
181.489 1
 
0.2%
180.098 1
 
0.2%
180.076 2
0.3%

type
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.0 KiB
audio_features
620 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters8680
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaudio_features
2nd rowaudio_features
3rd rowaudio_features
4th rowaudio_features
5th rowaudio_features

Common Values

ValueCountFrequency (%)
audio_features 620
100.0%

Length

2024-12-02T11:05:18.176030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:05:18.212448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
audio_features 620
100.0%

Most occurring characters

ValueCountFrequency (%)
a 1240
14.3%
u 1240
14.3%
e 1240
14.3%
d 620
7.1%
i 620
7.1%
o 620
7.1%
_ 620
7.1%
f 620
7.1%
t 620
7.1%
r 620
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8060
92.9%
Connector Punctuation 620
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1240
15.4%
u 1240
15.4%
e 1240
15.4%
d 620
7.7%
i 620
7.7%
o 620
7.7%
f 620
7.7%
t 620
7.7%
r 620
7.7%
s 620
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8060
92.9%
Common 620
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1240
15.4%
u 1240
15.4%
e 1240
15.4%
d 620
7.7%
i 620
7.7%
o 620
7.7%
f 620
7.7%
t 620
7.7%
r 620
7.7%
s 620
7.7%
Common
ValueCountFrequency (%)
_ 620
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1240
14.3%
u 1240
14.3%
e 1240
14.3%
d 620
7.1%
i 620
7.1%
o 620
7.1%
_ 620
7.1%
f 620
7.1%
t 620
7.1%
r 620
7.1%

id
Text

Distinct283
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size47.8 KiB
2024-12-02T11:05:18.335875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters13640
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)23.5%

Sample

1st row5vNRhkKd0yEAg8suGBpjeY
2nd row2plbrEY59IikOBgBGLjaoe
3rd row2CGNAOSuO1MEFCbBRgUzjd
4th row0nj9Bq5sHDiTxSHunhgkFb
5th row0aB0v4027ukVziUGwVGYpG
ValueCountFrequency (%)
5vnrhkkd0yeag8sugbpjey 12
 
1.9%
7ne4vba60cxgm75vw0eyad 12
 
1.9%
2262bwmqomiajxwcrhr13j 11
 
1.8%
2plbrey59iikobgbgljaoe 10
 
1.6%
6jbyprptefl1hfkhk1ic0m 9
 
1.5%
51rfrciusvxxlcscfiztby 8
 
1.3%
3qapy1kgi7nu9fjequgn6h 8
 
1.3%
1es7auahqvapicoh3qmkdl 8
 
1.3%
0wbmk4wrz1wfsty9f7fcgu 8
 
1.3%
6dotvtddiauqnbqedotlab 8
 
1.3%
Other values (273) 526
84.8%
2024-12-02T11:05:18.538406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 314
 
2.3%
0 304
 
2.2%
1 292
 
2.1%
7 285
 
2.1%
3 279
 
2.0%
6 270
 
2.0%
Y 267
 
2.0%
4 261
 
1.9%
R 260
 
1.9%
f 254
 
1.9%
Other values (52) 10854
79.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5511
40.4%
Uppercase Letter 5508
40.4%
Decimal Number 2621
19.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 267
 
4.8%
R 260
 
4.7%
B 253
 
4.6%
Q 247
 
4.5%
I 241
 
4.4%
K 238
 
4.3%
H 236
 
4.3%
F 236
 
4.3%
C 235
 
4.3%
T 230
 
4.2%
Other values (16) 3065
55.6%
Lowercase Letter
ValueCountFrequency (%)
f 254
 
4.6%
n 250
 
4.5%
j 242
 
4.4%
o 242
 
4.4%
e 237
 
4.3%
b 234
 
4.2%
p 230
 
4.2%
h 227
 
4.1%
y 225
 
4.1%
g 217
 
3.9%
Other values (16) 3153
57.2%
Decimal Number
ValueCountFrequency (%)
2 314
12.0%
0 304
11.6%
1 292
11.1%
7 285
10.9%
3 279
10.6%
6 270
10.3%
4 261
10.0%
5 251
9.6%
9 193
7.4%
8 172
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 11019
80.8%
Common 2621
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 267
 
2.4%
R 260
 
2.4%
f 254
 
2.3%
B 253
 
2.3%
n 250
 
2.3%
Q 247
 
2.2%
j 242
 
2.2%
o 242
 
2.2%
I 241
 
2.2%
K 238
 
2.2%
Other values (42) 8525
77.4%
Common
ValueCountFrequency (%)
2 314
12.0%
0 304
11.6%
1 292
11.1%
7 285
10.9%
3 279
10.6%
6 270
10.3%
4 261
10.0%
5 251
9.6%
9 193
7.4%
8 172
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 314
 
2.3%
0 304
 
2.2%
1 292
 
2.1%
7 285
 
2.1%
3 279
 
2.0%
6 270
 
2.0%
Y 267
 
2.0%
4 261
 
1.9%
R 260
 
1.9%
f 254
 
1.9%
Other values (52) 10854
79.6%

uri
Text

Distinct283
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
2024-12-02T11:05:18.704186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters22320
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)23.5%

Sample

1st rowspotify:track:5vNRhkKd0yEAg8suGBpjeY
2nd rowspotify:track:2plbrEY59IikOBgBGLjaoe
3rd rowspotify:track:2CGNAOSuO1MEFCbBRgUzjd
4th rowspotify:track:0nj9Bq5sHDiTxSHunhgkFb
5th rowspotify:track:0aB0v4027ukVziUGwVGYpG
ValueCountFrequency (%)
spotify:track:5vnrhkkd0yeag8sugbpjey 12
 
1.9%
spotify:track:7ne4vba60cxgm75vw0eyad 12
 
1.9%
spotify:track:2262bwmqomiajxwcrhr13j 11
 
1.8%
spotify:track:2plbrey59iikobgbgljaoe 10
 
1.6%
spotify:track:6jbyprptefl1hfkhk1ic0m 9
 
1.5%
spotify:track:51rfrciusvxxlcscfiztby 8
 
1.3%
spotify:track:3qapy1kgi7nu9fjequgn6h 8
 
1.3%
spotify:track:1es7auahqvapicoh3qmkdl 8
 
1.3%
spotify:track:0wbmk4wrz1wfsty9f7fcgu 8
 
1.3%
spotify:track:6dotvtddiauqnbqedotlab 8
 
1.3%
Other values (273) 526
84.8%
2024-12-02T11:05:18.899279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1441
 
6.5%
: 1240
 
5.6%
f 874
 
3.9%
o 862
 
3.9%
p 850
 
3.8%
y 845
 
3.8%
a 825
 
3.7%
k 817
 
3.7%
r 816
 
3.7%
i 815
 
3.7%
Other values (53) 12935
58.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12951
58.0%
Uppercase Letter 5508
24.7%
Decimal Number 2621
 
11.7%
Other Punctuation 1240
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1441
 
11.1%
f 874
 
6.7%
o 862
 
6.7%
p 850
 
6.6%
y 845
 
6.5%
a 825
 
6.4%
k 817
 
6.3%
r 816
 
6.3%
i 815
 
6.3%
s 810
 
6.3%
Other values (16) 3996
30.9%
Uppercase Letter
ValueCountFrequency (%)
Y 267
 
4.8%
R 260
 
4.7%
B 253
 
4.6%
Q 247
 
4.5%
I 241
 
4.4%
K 238
 
4.3%
H 236
 
4.3%
F 236
 
4.3%
C 235
 
4.3%
T 230
 
4.2%
Other values (16) 3065
55.6%
Decimal Number
ValueCountFrequency (%)
2 314
12.0%
0 304
11.6%
1 292
11.1%
7 285
10.9%
3 279
10.6%
6 270
10.3%
4 261
10.0%
5 251
9.6%
9 193
7.4%
8 172
6.6%
Other Punctuation
ValueCountFrequency (%)
: 1240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18459
82.7%
Common 3861
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1441
 
7.8%
f 874
 
4.7%
o 862
 
4.7%
p 850
 
4.6%
y 845
 
4.6%
a 825
 
4.5%
k 817
 
4.4%
r 816
 
4.4%
i 815
 
4.4%
s 810
 
4.4%
Other values (42) 9504
51.5%
Common
ValueCountFrequency (%)
: 1240
32.1%
2 314
 
8.1%
0 304
 
7.9%
1 292
 
7.6%
7 285
 
7.4%
3 279
 
7.2%
6 270
 
7.0%
4 261
 
6.8%
5 251
 
6.5%
9 193
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1441
 
6.5%
: 1240
 
5.6%
f 874
 
3.9%
o 862
 
3.9%
p 850
 
3.8%
y 845
 
3.8%
a 825
 
3.7%
k 817
 
3.7%
r 816
 
3.7%
i 815
 
3.7%
Other values (53) 12935
58.0%
Distinct283
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
https://api.spotify.com/v1/tracks/5vNRhkKd0yEAg8suGBpjeY
 
12
https://api.spotify.com/v1/tracks/7ne4VBA60CxGM75vw0EYad
 
12
https://api.spotify.com/v1/tracks/2262bWmqomIaJXwCRHr13j
 
11
https://api.spotify.com/v1/tracks/2plbrEY59IikOBgBGLjaoe
 
10
https://api.spotify.com/v1/tracks/6jbYpRPTEFl1HFKHk1IC0m
 
9
Other values (278)
566 
ValueCountFrequency (%)
https://api.spotify.com/v1/tracks/5vNRhkKd0yEAg8suGBpjeY 12
 
1.9%
https://api.spotify.com/v1/tracks/7ne4VBA60CxGM75vw0EYad 12
 
1.9%
https://api.spotify.com/v1/tracks/2262bWmqomIaJXwCRHr13j 11
 
1.8%
https://api.spotify.com/v1/tracks/2plbrEY59IikOBgBGLjaoe 10
 
1.6%
https://api.spotify.com/v1/tracks/6jbYpRPTEFl1HFKHk1IC0m 9
 
1.5%
https://api.spotify.com/v1/tracks/51rfRCiUSvxXlCSCfIztBy 8
 
1.3%
https://api.spotify.com/v1/tracks/3QaPy1KgI7nu9FJEQUgn6h 8
 
1.3%
https://api.spotify.com/v1/tracks/1Es7AUAhQvapIcoh3qMKDL 8
 
1.3%
https://api.spotify.com/v1/tracks/0WbMK4wrZ1wFSty9F7FCgu 8
 
1.3%
https://api.spotify.com/v1/tracks/6dOtVTDdiauQNBQEDOtlAB 8
 
1.3%
Other values (273) 526
84.8%
ValueCountFrequency (%)
https 620
100.0%
ValueCountFrequency (%)
api.spotify.com 620
100.0%
ValueCountFrequency (%)
/v1/tracks/5vNRhkKd0yEAg8suGBpjeY 12
 
1.9%
/v1/tracks/7ne4VBA60CxGM75vw0EYad 12
 
1.9%
/v1/tracks/2262bWmqomIaJXwCRHr13j 11
 
1.8%
/v1/tracks/2plbrEY59IikOBgBGLjaoe 10
 
1.6%
/v1/tracks/6jbYpRPTEFl1HFKHk1IC0m 9
 
1.5%
/v1/tracks/51rfRCiUSvxXlCSCfIztBy 8
 
1.3%
/v1/tracks/3QaPy1KgI7nu9FJEQUgn6h 8
 
1.3%
/v1/tracks/1Es7AUAhQvapIcoh3qMKDL 8
 
1.3%
/v1/tracks/0WbMK4wrZ1wFSty9F7FCgu 8
 
1.3%
/v1/tracks/6dOtVTDdiauQNBQEDOtlAB 8
 
1.3%
Other values (273) 526
84.8%
ValueCountFrequency (%)
620
100.0%
ValueCountFrequency (%)
620
100.0%
Distinct283
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
https://api.spotify.com/v1/audio-analysis/5vNRhkKd0yEAg8suGBpjeY
 
12
https://api.spotify.com/v1/audio-analysis/7ne4VBA60CxGM75vw0EYad
 
12
https://api.spotify.com/v1/audio-analysis/2262bWmqomIaJXwCRHr13j
 
11
https://api.spotify.com/v1/audio-analysis/2plbrEY59IikOBgBGLjaoe
 
10
https://api.spotify.com/v1/audio-analysis/6jbYpRPTEFl1HFKHk1IC0m
 
9
Other values (278)
566 
ValueCountFrequency (%)
https://api.spotify.com/v1/audio-analysis/5vNRhkKd0yEAg8suGBpjeY 12
 
1.9%
https://api.spotify.com/v1/audio-analysis/7ne4VBA60CxGM75vw0EYad 12
 
1.9%
https://api.spotify.com/v1/audio-analysis/2262bWmqomIaJXwCRHr13j 11
 
1.8%
https://api.spotify.com/v1/audio-analysis/2plbrEY59IikOBgBGLjaoe 10
 
1.6%
https://api.spotify.com/v1/audio-analysis/6jbYpRPTEFl1HFKHk1IC0m 9
 
1.5%
https://api.spotify.com/v1/audio-analysis/51rfRCiUSvxXlCSCfIztBy 8
 
1.3%
https://api.spotify.com/v1/audio-analysis/3QaPy1KgI7nu9FJEQUgn6h 8
 
1.3%
https://api.spotify.com/v1/audio-analysis/1Es7AUAhQvapIcoh3qMKDL 8
 
1.3%
https://api.spotify.com/v1/audio-analysis/0WbMK4wrZ1wFSty9F7FCgu 8
 
1.3%
https://api.spotify.com/v1/audio-analysis/6dOtVTDdiauQNBQEDOtlAB 8
 
1.3%
Other values (273) 526
84.8%
ValueCountFrequency (%)
https 620
100.0%
ValueCountFrequency (%)
api.spotify.com 620
100.0%
ValueCountFrequency (%)
/v1/audio-analysis/5vNRhkKd0yEAg8suGBpjeY 12
 
1.9%
/v1/audio-analysis/7ne4VBA60CxGM75vw0EYad 12
 
1.9%
/v1/audio-analysis/2262bWmqomIaJXwCRHr13j 11
 
1.8%
/v1/audio-analysis/2plbrEY59IikOBgBGLjaoe 10
 
1.6%
/v1/audio-analysis/6jbYpRPTEFl1HFKHk1IC0m 9
 
1.5%
/v1/audio-analysis/51rfRCiUSvxXlCSCfIztBy 8
 
1.3%
/v1/audio-analysis/3QaPy1KgI7nu9FJEQUgn6h 8
 
1.3%
/v1/audio-analysis/1Es7AUAhQvapIcoh3qMKDL 8
 
1.3%
/v1/audio-analysis/0WbMK4wrZ1wFSty9F7FCgu 8
 
1.3%
/v1/audio-analysis/6dOtVTDdiauQNBQEDOtlAB 8
 
1.3%
Other values (273) 526
84.8%
ValueCountFrequency (%)
620
100.0%
ValueCountFrequency (%)
620
100.0%

duration_ms
Real number (ℝ)

Distinct251
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201369.34
Minimum70767
Maximum587365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2024-12-02T11:05:18.972260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70767
5-th percentile130412.15
Q1164773
median186365
Q3228948
95-th percentile284239.8
Maximum587365
Range516598
Interquartile range (IQR)64175

Descriptive statistics

Standard deviation56765.46
Coefficient of variation (CV)0.28189724
Kurtosis6.0382811
Mean201369.34
Median Absolute Deviation (MAD)30448
Skewness1.5551238
Sum1.2484899 × 108
Variance3.2223175 × 109
MonotonicityNot monotonic
2024-12-02T11:05:19.026876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169917 12
 
1.9%
166300 12
 
1.9%
211979 12
 
1.9%
251668 10
 
1.6%
278015 9
 
1.5%
180304 8
 
1.3%
186365 8
 
1.3%
157147 8
 
1.3%
256000 8
 
1.3%
157280 8
 
1.3%
Other values (241) 525
84.7%
ValueCountFrequency (%)
70767 1
 
0.2%
86984 5
0.8%
87835 1
 
0.2%
94946 1
 
0.2%
101533 4
0.6%
104419 2
 
0.3%
115533 4
0.6%
122361 1
 
0.2%
123591 2
 
0.3%
125611 1
 
0.2%
ValueCountFrequency (%)
587365 1
 
0.2%
459766 2
0.3%
457333 2
0.3%
454779 1
 
0.2%
400533 2
0.3%
383067 1
 
0.2%
353920 4
0.6%
317093 3
0.5%
316000 2
0.3%
311480 1
 
0.2%

time_signature
Categorical

Imbalance 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
4
549 
3
68 
5
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters620
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 549
88.5%
3 68
 
11.0%
5 3
 
0.5%

Length

2024-12-02T11:05:19.074722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:05:19.111222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 549
88.5%
3 68
 
11.0%
5 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
4 549
88.5%
3 68
 
11.0%
5 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 549
88.5%
3 68
 
11.0%
5 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 549
88.5%
3 68
 
11.0%
5 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 549
88.5%
3 68
 
11.0%
5 3
 
0.5%

Playlist
Categorical

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size42.3 KiB
Hot_Hits_UK
70 
Top50-Global
50 
Top_Songs-USA
50 
Viral50-USA
50 
Viral50-Global
50 
Other values (7)
350 

Length

Max length19
Median length14
Mean length12.854839
Min length8

Characters and Unicode

Total characters7970
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTop50-Global
2nd rowTop50-Global
3rd rowTop50-Global
4th rowTop50-Global
5th rowTop50-Global

Common Values

ValueCountFrequency (%)
Hot_Hits_UK 70
11.3%
Top50-Global 50
8.1%
Top_Songs-USA 50
8.1%
Viral50-USA 50
8.1%
Viral50-Global 50
8.1%
Viral50-Singapore 50
8.1%
Top_Songs-Singapore 50
8.1%
Top50-USA 50
8.1%
Top50-Singapore 50
8.1%
Viral50-UK 50
8.1%
Other values (2) 100
16.1%

Length

2024-12-02T11:05:19.154871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hot_hits_uk 70
11.3%
top50-global 50
8.1%
top_songs-usa 50
8.1%
viral50-usa 50
8.1%
viral50-global 50
8.1%
viral50-singapore 50
8.1%
top_songs-singapore 50
8.1%
top50-usa 50
8.1%
top50-singapore 50
8.1%
viral50-uk 50
8.1%
Other values (2) 100
16.1%

Most occurring characters

ValueCountFrequency (%)
o 820
 
10.3%
a 500
 
6.3%
p 500
 
6.3%
- 500
 
6.3%
i 470
 
5.9%
0 400
 
5.0%
T 400
 
5.0%
5 400
 
5.0%
l 400
 
5.0%
S 400
 
5.0%
Other values (17) 3180
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4350
54.6%
Uppercase Letter 1930
24.2%
Decimal Number 800
 
10.0%
Dash Punctuation 500
 
6.3%
Connector Punctuation 340
 
4.3%
Other Punctuation 50
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 820
18.9%
a 500
11.5%
p 500
11.5%
i 470
10.8%
l 400
9.2%
r 350
8.0%
s 270
 
6.2%
g 250
 
5.7%
n 250
 
5.7%
t 190
 
4.4%
Other values (4) 350
8.0%
Uppercase Letter
ValueCountFrequency (%)
T 400
20.7%
S 400
20.7%
U 320
16.6%
V 200
10.4%
H 190
9.8%
K 170
8.8%
A 150
 
7.8%
G 100
 
5.2%
Decimal Number
ValueCountFrequency (%)
0 400
50.0%
5 400
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 340
100.0%
Other Punctuation
ValueCountFrequency (%)
' 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6280
78.8%
Common 1690
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 820
13.1%
a 500
 
8.0%
p 500
 
8.0%
i 470
 
7.5%
T 400
 
6.4%
l 400
 
6.4%
S 400
 
6.4%
r 350
 
5.6%
U 320
 
5.1%
s 270
 
4.3%
Other values (12) 1850
29.5%
Common
ValueCountFrequency (%)
- 500
29.6%
0 400
23.7%
5 400
23.7%
_ 340
20.1%
' 50
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 820
 
10.3%
a 500
 
6.3%
p 500
 
6.3%
- 500
 
6.3%
i 470
 
5.9%
0 400
 
5.0%
T 400
 
5.0%
5 400
 
5.0%
l 400
 
5.0%
S 400
 
5.0%
Other values (17) 3180
39.9%

Region
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
UK
170 
Global
150 
USA
150 
Singapore
150 

Length

Max length9
Median length6
Mean length4.9032258
Min length2

Characters and Unicode

Total characters3040
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGlobal
2nd rowGlobal
3rd rowGlobal
4th rowGlobal
5th rowGlobal

Common Values

ValueCountFrequency (%)
UK 170
27.4%
Global 150
24.2%
USA 150
24.2%
Singapore 150
24.2%

Length

2024-12-02T11:05:19.202218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:05:19.244086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
uk 170
27.4%
global 150
24.2%
usa 150
24.2%
singapore 150
24.2%

Most occurring characters

ValueCountFrequency (%)
U 320
10.5%
l 300
 
9.9%
o 300
 
9.9%
a 300
 
9.9%
S 300
 
9.9%
K 170
 
5.6%
G 150
 
4.9%
b 150
 
4.9%
A 150
 
4.9%
i 150
 
4.9%
Other values (5) 750
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1950
64.1%
Uppercase Letter 1090
35.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 300
15.4%
o 300
15.4%
a 300
15.4%
b 150
7.7%
i 150
7.7%
n 150
7.7%
g 150
7.7%
p 150
7.7%
r 150
7.7%
e 150
7.7%
Uppercase Letter
ValueCountFrequency (%)
U 320
29.4%
S 300
27.5%
K 170
15.6%
G 150
13.8%
A 150
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3040
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 320
10.5%
l 300
 
9.9%
o 300
 
9.9%
a 300
 
9.9%
S 300
 
9.9%
K 170
 
5.6%
G 150
 
4.9%
b 150
 
4.9%
A 150
 
4.9%
i 150
 
4.9%
Other values (5) 750
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 320
10.5%
l 300
 
9.9%
o 300
 
9.9%
a 300
 
9.9%
S 300
 
9.9%
K 170
 
5.6%
G 150
 
4.9%
b 150
 
4.9%
A 150
 
4.9%
i 150
 
4.9%
Other values (5) 750
24.7%

Interactions

2024-12-02T11:05:16.053079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:09.799352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.449056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.954855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.376442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.508943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.979551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.498061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.120585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.601236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.078598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.094607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:09.998405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.494632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.998992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.420921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.553578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.054209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.542658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.169960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.642571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.122452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.130391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.036005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.533309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.037835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.456461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.589813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.096948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.582502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.206601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.679935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.159529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.170947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.075053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.570173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.073652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.490535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.627636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.138407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.627491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.245226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.718253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.207889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.206849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.111565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.605407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.108149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.917058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.665594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.185399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.677599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.282629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.755676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.247173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.244978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.151510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.648665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.145838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.100806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.705559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.224987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.717695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.321610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.810478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.285466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.286109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.202827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.690409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.182731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.258995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.743896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.263654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.909979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.363447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.854330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.322903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.323155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.242544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.739717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.217425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.340968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.788479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.301951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.954650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.412124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.894249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.725381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.365224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.284863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.803131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.259432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.393483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.834702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.351675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.998695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.457528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.944955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.861130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.403556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.337803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.842636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.295653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.431442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.875049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.400503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.038339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.502042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.992607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.937489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.451108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.398482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:10.883495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:11.333672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.468650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:12.916035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:13.453797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.077434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:14.551869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:15.033970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:05:16.003844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Missing values

2024-12-02T11:05:16.517079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-02T11:05:16.667348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeiduritrack_hrefanalysis_urlduration_mstime_signaturePlaylistRegion
00.7770.7830-4.47700.26000.028300.0000000.35500.939149.027audio_features5vNRhkKd0yEAg8suGBpjeYspotify:track:5vNRhkKd0yEAg8suGBpjeYhttps://api.spotify.com/v1/tracks/5vNRhkKd0yEAg8suGBpjeYhttps://api.spotify.com/v1/audio-analysis/5vNRhkKd0yEAg8suGBpjeY1699174Top50-GlobalGlobal
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40.8550.5296-6.67900.26300.083700.0000000.42300.548100.036audio_features0aB0v4027ukVziUGwVGYpGspotify:track:0aB0v4027ukVziUGwVGYpGhttps://api.spotify.com/v1/tracks/0aB0v4027ukVziUGwVGYpGhttps://api.spotify.com/v1/audio-analysis/0aB0v4027ukVziUGwVGYpG2206904Top50-GlobalGlobal
50.7470.5072-10.17110.03580.200000.0608000.11700.438104.978audio_features6dOtVTDdiauQNBQEDOtlABspotify:track:6dOtVTDdiauQNBQEDOtlABhttps://api.spotify.com/v1/tracks/6dOtVTDdiauQNBQEDOtlABhttps://api.spotify.com/v1/audio-analysis/6dOtVTDdiauQNBQEDOtlAB2103734Top50-GlobalGlobal
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90.7610.5019-10.75900.45900.249000.0000000.13600.50281.998audio_features5gOfC9UzZQzTyShqPMrpjTspotify:track:5gOfC9UzZQzTyShqPMrpjThttps://api.spotify.com/v1/tracks/5gOfC9UzZQzTyShqPMrpjThttps://api.spotify.com/v1/audio-analysis/5gOfC9UzZQzTyShqPMrpjT3170934Top50-GlobalGlobal
danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotypeiduritrack_hrefanalysis_urlduration_mstime_signaturePlaylistRegion
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